Two-dimensional Renyi entropic threshold segmentation method for grayscale images

Abstract

The invention discloses a two-dimensional Renyi entropic threshold segmentation method for grayscale images, which comprises the following steps: carrying out median filtering on an input original image I so as to obtain an image I_med subjected to median filtering; based on the original image I and the image I_med subjected to median filtering, constructing a grayscale median two-dimensional histogram; carrying out optimization on the obtained two-dimensional histogram by using an inertia weight increasing strategy introduced particle swarm method so as to obtain the best threshold and the optimal segmentation point; and according to the best threshold and the optimal segmentation point, carrying out segmentation on the image. According to the invention, through introducing a median filtering technology with good robustness in image filtering, replacing a window grayscale mean in the traditional method with a neighborhood window grayscale median, and combining with the original image, a novel two-dimensional histogram is constructed; an objective function is optimized by using the inertia weight increasing strategy introduced particle swarm method, so that the effective rapid segmentation is performed under noisy conditions, thereby accelerating the convergence speed and improving the segmentation efficiency.

Description

technical field [0001] The invention relates to the technical field of image processing and machine vision, in particular to a two-dimensional Renyi entropy threshold segmentation method for grayscale images, which is mainly applied to the visual processing of robots. Background technique [0002] The development of robot technology is an important symbol and embodiment of a country's high-tech level and industrial automation. Robots are more and more widely used in current production and life, and are playing an increasingly important role in replacing humans. Among them, robot vision is considered to be the most important perception part of robots. Robot vision is the embodiment of simulating human vision on the robot. With the help of advanced computers and processors, digital image technology is analyzed through digital technology to realize the recognition of the shape and motion of the scenery and objects in the objective world. Therefore, image segmentation has becom...

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Problems solved by technology

But this method has the following two important shortcomings: First, the two-dimensional histogram it uses is based on the gray mean value of the neighborhood window. If the neighborhood window of a certain pixel in the image is polluted by salt and pepper noise, its neighbors The average gray value of the domain window will have a large change, which will bring large distortion to the two-dimensional histogram, thus affecting the final image segmentation effect

Secondly, when optimizing the two-dimensional Renyi entropy objective function, the introduction of the two-dimensional histogram expands the search space from one dimension to two dimensions. If the traditional exhaustive search strategy is used, the amount of calculation is huge, making it less practical. limitation

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